Splicing efficiency varies among transcripts, and tight control of splicing kinetics is crucial for coordinated gene expression. N-6-methyladenosine (m6A) is the most abundant RNA modification and is involved in regulation of RNA biogenesis and function. The impact of m6A on regulation of RNA splicing kinetics is unknown. Here, we provide a time-resolved high-resolution assessment of m6A on nascent RNA transcripts and unveil its importance for the control of RNA splicing kinetics. We find that early co-transcriptional m6A deposition near splice junctions promotes fast splicing, while m6A modifications in introns are associated with long, slowly processed introns and alternative splicing events. In conclusion, we show that early m6A deposition specifies the fate of transcripts regarding splicing kinetics and alternative splicing.

Oncogene-induced senescence (OIS), provoked in response to oncogenic activation, is considered an important tumor suppressor mechanism. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without a protein-coding capacity. Functional studies showed that deregulated lncRNA expression promote tumorigenesis and metastasis and that lncRNAs may exhibit tumor-suppressive and oncogenic function. Here, we first identified lncRNAs that were differentially expressed between senescent and non-senescent human fibroblast cells. Using RNA interference, we performed a loss-function screen targeting the differentially expressed lncRNAs, and identified lncRNA-OIS1 (lncRNA#32, AC008063.3 or ENSG00000233397) as a lncRNA required for OIS. Knockdown of lncRNA-OIS1 triggered bypass of senescence, higher proliferation rate, lower abundance of the cell-cycle inhibitor CDKN1A and high expression of cell-cycle-associated genes. Subcellular inspection of lncRNA-OIS1 indicated nuclear and cytosolic localization in both normal culture conditions as well as following oncogene induction. Interestingly, silencing lncRNA-OIS1 diminished the senescent-associated induction of a nearby gene (Dipeptidyl Peptidase 4, DPP4) with established role in tumor suppression. Intriguingly, similar to lncRNA-OIS1, silencing DPP4 caused senescence bypass, and ectopic expression of DPP4 in lncRNA-OIS1 knockdown cells restored the senescent phenotype. Thus, our data indicate that lncRNA-OIS1 links oncogenic induction and senescence with the activation of the tumor suppressor DPP4.

Long ncRNAs are often enriched in the nucleus and at chromatin, but whether their dissociation from chromatin is important for their role in transcription regulation is unclear. Here, we group long ncRNAs using epigenetic marks, expression and strength of chromosomal interactions; we find that long ncRNAs transcribed from loci engaged in strong long-range chromosomal interactions are less abundant at chromatin, suggesting the release from chromatin as a crucial functional aspect of long ncRNAs in transcription regulation of their target genes. To gain mechanistic insight into this, we functionally validate the long ncRNA A-ROD, which enhances DKK1 transcription via its nascent spliced released form. Our data provide evidence that the regulatory interaction requires dissociation of A-ROD from chromatin, with target specificity ensured within the pre-established chromosomal proximity. We propose that the post-transcriptional release of a subset of long ncRNAs from the chromatin-associated template plays an important role in their function as transcription regulators.

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.